Predicting when and how collections of particles, robots or animals become ordered remains a challenge for science and engineering.
In the 19th century, scientists and engineers developed the discipline of statistical mechanics, which predicts how groups of single particles go from order to disorder, such as when a collection of randomly colliding atoms freezes to form a uniform crystal lattice.
The collective behaviors that can be achieved when particles become more complicated, so that they can move on their own, are more difficult to predict. This type of system – seen in flocks of birds, bacterial colonies and swarms of robots – is called the “active ingredient”.
As reported in the January 1, 2021 issue of the journal Science, a team of physicists and engineers have come up with a new principle whereby active material systems can control spontaneously, without the need for higher level instructions or even programmed interaction between agents. And they’ve demonstrated this principle in a variety of systems, including groups of shape-changing robots called “smarticles” – intelligent, active particles.
The theory, developed by Dr. Pavel Chvykov at the Massachusetts Institute of Technology while a student of Professor Jeremy England, who is now a researcher at the School of Physics at the Georgia Institute of Technology, postulates that certain types of active matter with enough disorder dynamics will spontaneously find what researchers call “low-clicking” states.
“Clicking is when matter takes the energy flowing through it and turns it into random motion,” England said. “The clicking noise may be greater when the movement is more violent, or more random. Conversely, a weak clicking noise is either very light or very organized – or both. So the idea is that if your matter and your source d energy allow a low knocking state, the system will randomly rearrange itself until it finds that state, and then hang in it. If you are providing energy by forces with a particular pattern, it means that the ‘selected state will discover a way for matter to move that finely matches this pattern. “
To develop their theory, England and Chvykov were inspired by a phenomenon – nicknamed – discovered by the Swiss physicist Charles Soret at the end of the 19th century. In Soret’s experiments, he found that subjecting an initially uniform saline solution in a tube to a temperature difference would spontaneously lead to an increase in the salt concentration in the cooler region – which corresponds to an increase of the order of the solution.
Chvykov and England developed many mathematical models to demonstrate the low knock principle, but it was only after contacting Daniel Goldman, professor of physics in the Dunn family at the Georgia Institute of Technology, that they were able to test their predictions.
Goldman said: “A few years ago I saw England give a seminar and thought some of our smarticle robots might be useful in testing this theory.” Together with Chvykov, who visited Goldman’s lab, Ph.D. students William Savoie and Akash Vardhan used three beating smarticles encased in a ring to compare experiments to theory. Students observed that instead of displaying complicated dynamics and fully exploring the container, the robots spontaneously self-organize into a few dances – for example, a dance consists of three robots slapping their arms each other. after another. These dances can persist for hundreds of strands, but suddenly lose their stability and are replaced by a dance of a different pattern.
After first demonstrating that these simple dances were indeed low noise states, Chvykov worked with engineers from Northwestern University, Professor Todd Murphey and a PhD. student Thomas Berrueta, who developed more refined and better controlled smarticles. The improved smarticles allowed researchers to test the limits of the theory, including how the types and number of dances varied for different patterns of arm kicks, as well as how these dances could be controlled. “By controlling the low knocking state sequences, we were able to get the system to achieve configurations that do useful work,” Berrueta said. Northwestern University researchers say these findings could have broad practical implications for microrobotic swarms, active ingredient, and metamaterials.
As England noted: “For robot swarms, it’s about getting a lot of adaptive and intelligent group behaviors that you can design to be done in one swarm, even though the robots individual products are relatively inexpensive and easy to calculate. For living cells and new materials, it might be to figure out what the “swarm” of atoms or proteins can do for you, in terms of new material or computational properties. “
The Georgia Tech-based team for the study includes Jeremy L. England, a living systems physics scientist who conducts research with the School of Physics, Dunn family professor Daniel Goldman, Professor Kurt Wiesenfeld and the students. graduates Akash Vardhan (quantitative biosciences) and William Savoie School of Physics). They join graduate student Pavel Chvykov (Massachusetts Institute of Technology), as well as Professor Todd D. Murphey and graduate students Thomas A. Berrueta and Alexander Samland from Northwestern University.
This material is based on the work supported by the Army Research Bureau under the awards of ARO W911NF-18-1-0101, ARO MURI Award W911NF-19-1-0233, ARO W911NF-13-1-0347, by the National Science Foundation under grant PoLS-0957659, PHY-1205878, PHY-1205878, PHY-1205878 and DMR-1551095, NSF CBET-1637764, by the James S. McDonnell Foundation Scholar Grant 220020476, and the Georgia Institute of Technology Dunn Family Professorship. All opinions, findings and conclusions or recommendations expressed in this document are those of the authors and do not necessarily reflect the views of the sponsoring agencies.
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